Structured lexical similarity via convolution Kernels on dependency trees

A central topic in natural language process-ing is the design of lexical and syntactic fea-tures suitable for the target application. In this paper, we study convolution dependency tree kernels for automatic engineering of syntactic and semantic patterns exploiting lexical simi-larities. We define efficient and powerful ker-nels for measuring the similarity between de-pendency structures, whose surface forms of the lexical nodes are in part or completely dif-ferent. The experiments with such kernels for question classification show an unprecedented results, e.g. 41 % of error reduction of the for-mer state-of-the-art. Additionally, semantic role classification confirms the benefit of se-mantic smoothing for dependency kernels.

Shake table testing of a tuned mass damper inerter (Tmdi)-equipped structure and nonlinear dynamic modeling under harmonic excitations

This paper presents preliminary experimental results from a novel shaking table testing campaign investigating the dynamic response of a two-degree-of-freedom (2DOF) physical specimen with a grounded inerter under harmonic base excitation and contributes a nonlinear dynamic model capturing the behavior of the test specimen. The latter consists of a primary mass connected to the ground through a high damping rubber isolator (HDRI) and a secondary mass connected to the primary mass through a second HDRI. Further, a flywheel-based rack-and-pinion inerter prototype device is used to connect the secondary mass to the ground. The resulting specimen resembles the tuned mass damper inerter (TMDI) configuration with grounded inerter analytically defined and numerically assessed by the authors in a number of previous publications. Physical specimens with three different inerter coefficients are tested on the shake table under sine-sweep excitation with three different amplitudes. Experimental frequency response functions (FRFs) are derived manifesting a softening nonlinear behavior of the specimens and enhanced vibration suppression with increased inerter coefficient. Further, a 2DOF parametric nonlinear model of the specimen is established accounting for non-ideal inerter device behavior and its potential to characterize experimental response time-histories, FRFs, and force-displacement relationships of the HDRIs and of the inerter is verified

Automatic induction of framenet lexical units in Italian

In this paper we investigate the applicability of automatic methods for frame induction to improve the coverage of IFrameNet, a novel lexical resource based on Frame Semantics in Italian. The experimental evaluations show that the adopted methods based on neural word embeddings pave the way for the assisted development of a large scale lexical resource for our language

UNITOR @ DANKMEMES: Combining convolutional models and transformer-based architectures for accurate MEME management

This paper describes the UNITOR system that participated to the “multimoDal Artefacts recogNition Knowledge for MEMES” (DANKMEMES) task within the context of EVALITA 2020. UNITOR implements a neural model which combines a Deep Convolutional Neural Network to encode visual information of input images and a Transformer-based architecture to encode the meaning of the attached texts. UNITOR ranked first in all subtasks, clearly confirming the robustness of the investigated neural architectures and suggesting the beneficial impact of the proposed combination strategy

A Systems Biology approach to understanding and monitoring chemical toxicity in the environment

Chemicals pose every day a continuous hazard to both human health and environment. Unfortunately, Information about chemicals Mode of Action (MoA) for most of these compounds is limited. Development of approaches able to elucidate chemicals mechanisms of action is needed in order to improve risk assessment. Environmental omics aims to provide tools and methodologies to address these goals. Omics technologies in combination with system biology approaches have the potential to provide a powerful toolbox for understanding chemicals mode of action and consequently the outcomes these compounds trigger. The work presented in this thesis demonstrates the effectiveness of such approach in the context of environmentally relevant species. More specifically I focused on characterization of single chemical and chemical class toxicity mechanism in zebrafish embryos (Danio rerio) and in a fish gill cell line (Rainbow trout) and I demonstrated that the transcriptional state of an in vitro system exposed to a panel of environmentally relevant chemicals can be used as a biosensor to predict toxicity in an in vivo system. I also developed a computational model of ovary development in Largemouth bass (Micropterus salmoides) and used this to successfully identify chemical compounds with the ability to affect reproduction. Lastly, I developed a method to identify novel endocrine disrupting compounds in Daphnia magna supporting the use of this species for rapid screening in risk assessment. My results demonstrated the potential of system biology and data-driven science in identifying novel mechanisms of environmental toxicity and to develop a set of biomarkers for monitoring purposes. Further development building on these findings could potentially lead to improvements in risk assessment

A smoothed stochastic earthquake rate model considering seismicity and fault moment release for Europe

We present a time-independent gridded earthquake rate forecast for the European region including Turkey. The spatial component of our model is based on kernel density estimation techniques, which we applied to both past earthquake locations and fault moment release on mapped crustal faults and subduction zone interfaces with assigned slip rates. Our forecast relies on the assumption that the locations of past seismicity is a good guide to future seismicity, and that future large-magnitude events occur more likely in the vicinity of known faults. We show that the optimal weighted sum of the corresponding two spatial densities depends on the magnitude range considered. The kernel bandwidths and density weighting function are optimized using retrospective likelihood-based forecast experiments. We computed earthquake activity rates (a- and b-value) of the truncated Gutenberg-Richter distribution separately for crustal and subduction seismicity based on a maximum likelihood approach that considers the spatial and temporal completeness history of the catalogue. The final annual rate of our forecast is purely driven by the maximum likelihood fit of activity rates to the catalogue data, whereas its spatial component incorporates contributions from both earthquake and fault moment-rate densities. Our model constitutes one branch of the earthquake source model logic tree of the 2013 European seismic hazard model released by the EU-FP7 project ‘Seismic HAzard haRmonization in Europe' (SHARE) and contributes to the assessment of epistemic uncertainties in earthquake activity rates. We performed retrospective and pseudo-prospective likelihood consistency tests to underline the reliability of our model and SHARE's area source model (ASM) using the testing algorithms applied in the collaboratory for the study of earthquake predictability (CSEP). We comparatively tested our model's forecasting skill against the ASM and find a statistically significant better performance for testing periods of 10-20yr. The testing results suggest that our model is a viable candidate model to serve for long-term forecasting on timescales of years to decades for the European regio